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A B ×20 ×50 ×100 100% 1000 800 90% 600 80% 400 Mean depth 70% 200 0 60% 50% Mean coverage 40% PBL002_T PBL003_T PBL004_T PBL005_T PBL006_T PBL007_T PBL008_T PBL009_T PBL010_T PBL001_P 30% 20% 10% 0% PBL002_T PBL003_T PBL004_T PBL005_T PBL006_T PBL007_T PBL008_T PBL009_T PBL010_T PBL001_P Figure S1. Depths and coverages of targeted deep sequencing. A. Depths of targeted deep sequencing on 10 PBL cases (mean depth = 677). B. Coverages of the targeted sequences analyzed at the indicated depths; 98.8% of the targeted regions were sequenced at a depth more than 100×. A B ×2 ×10 ×20 250 100% 200 90% 150 80% 100 Mean depth 50 70% 0 60% 50% PBL002_T PBL004_T PBL007_T PBL008_T PBL010_T PBL001_P PBL001_N PBL002_N PBL004_N PBL007_N PBL008_N PBL010_N PBL001_M1 PBL001_M2 PBL001_M3 PBL001_M4 PBL001_M5 PBL001_M6 Mean coverage 40% 30% 20% 10% 0% PBL002_T PBL004_T PBL007_T PBL008_T PBL010_T PBL001_P PBL001_N PBL002_N PBL004_N PBL007_N PBL008_N PBL010_N PBL001_M1 PBL001_M2 PBL001_M3 PBL001_M4 PBL001_M5 PBL001_M6 C D 14 80 12 70 10 60 50 8 40 6 30 4 Number of mutations Number of mutations 20 2 10 0 0 PBL002_T PBL004_T PBL007_T PBL008_T PBL010_T PBL001_P PBL001_P PBL001_M1 PBL001_M2 PBL001_M3 PBL001_M4 PBL001_M5 PBL001_M6 Nonsynonymous SNV Stopgain SNV Nonframeshift indels Frameshift indels Splice site Figure S2. Sequencing coverages and numbers of mutations detected by WES. A. Depths of WES on tumor and normal DNA samples from 6 cases (mean depth = 124 for tumor samples and 141 for normal samples). B. Coverages of the exome sequences; 97.5% of the exome sequences were analyzed at a depth more than 20×. C-D. The number of somatic non-silent mutations and indels detected by WES and validated by PCR-based amplicon deep sequencing in 6 primary tumor samples (C) and 7 multiple samples from PBL001 (D). SNV, single-nucleotide variant. P M1 M2 M3 CLEC19A NOTCH1 SIK3 MFSD2B CTNNB1 UBE2E2 GOLM1 PTPRU CTSW KIAA0368 UBE2E2 UBE2E2 PTPRU CCDC88C CTNNB1 KDM6A CTNNB1 MRGPRE BRS3 CCDC88C SYNJ1 OR4D11 KDM6A OR4D11 CCDC88C DSP KDM6A MRGPRE PLXNB1 CTNNB1 CCDC88C SIK3 ZC3H18 OR4D11 ABCA13 PGK2_c.A155T NTNG1_c.A300T HUWE1 OR4D11 LEF1 MAGEB1 PGK2_c.G154A KLKB1 NTNG1_c.C301T LRRIQ3 TMEM132C_c.A2550G UBE2E2 PHKA2 EXPH5 ADGRG2 MUC3A ADCY1 BRWD1 ZBED2 FOXP4 MRGPRE LCN9 TMPRSS5 SIK3 MYBPC1 IQCF1 GRM2 CYP2W1 TMEM132C_c.G2551T MRGPRE ABHD12B KDM6A SERPINB7 FRMD6 HECW1 PTPRU HOXB13 LOC650293 ZNF804B MYO6 XCR1 SIK3 GLRA3 MADD KMT2A CFAP43 NARFL DNAH17 EDN2 CUX1 PCLO HIST1H4D NUDCD3 PTPRU SLC5A10 NLRP10 PCNXL3 DLL1 AOC1 CNBP IFT43 GBF1 CEP295 GSX2 MKI67 CADPS2 ASCC3 KRTAP13-2 GALR1 CELSR1 DMBT1 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Cellular Frequency Cellular Frequency Cellular Frequency Cellular Frequency M4 M5 M6 UBE2U SIK3 PKP3 KAT6B PTPRU MVK PIK3CD OGN CBS UBE2E2 OR51T1 KDM6A CCDC88C ADGB SIK3 KCNK17 ZBTB38 PPL MRGPRE BTBD16 MGMT OGN CTNNB1 OR51M1 MRGPRE CCDC88C MRGPRE GPR149 CTNNB1 OR51M1 FANCG OGN KDM6A OR51B4 SYNE1 KCTD1 UBE2E2 GLI3 PTPRU OR4D11 MLLT10 NPSR1 ARHGAP29 OR4D11 CUTC CDH3 PABPN1L UBE2E2 AHNAK OR4D11 DGCR14 TNXB OR51T1 ABCD4 SIK3 KDM6A GART PTPRU SETD4 ATR TIGD3 RAB11FIP3_c.C379A ZFHX3 OR51T1 CUL9 OR51M1 STIM1 CDKN2AIP GBP5 CTNNB1 XKRX ZNF516 RAB11FIP3_c.C380A TRNT1 KIAA2026 ADGRG4 RNF40 TRH HNRNPCL2 FA2H NTAN1 SLC17A6 MOXD1 SEMA3E CYSRT1 ZCCHC11 CD109 RPS6KA5 KCNQ5 COL6A5 RBM25 DMBT1 RANBP10 ALG1L TRH DLK1 LAMC3 KIAA1462 SLC34A2 TAF7L RNF38 HECW2 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 Cellular Frequency Cellular Frequency Cellular Frequency Figure S3. Tumor subclonal populations in multiple samples from PBL001 estimated by PyClone. Cellular frequencies of detected somatic mutaions in each sample from PBL001 are shown on each panel. Mutations on chromosomal regions with copy number gains were not used for the analysis because mutant cell fractions may be underestimated. P, primary sample; M1-M6, metastatic samples. B A Probability Probability 0.00 0.05 0.10 0.15 0.00 0.05 0.10 0.15 for primary(left),andmetastaticPBL(right). substitution patterns,the±2flankingbases,and transcriptionstranddirections,plotted immediately 3’and5’tothemutatedbase.B.Mutational signaturesconsistingofthe of substitutions.Eachsubstitutiontypecontains16 barsrepresentingthesequencecontext samples fromPBL001(lowerplot).Theprobability bars arecoloredaccordingtothe6types of 6primaryPBLsamplesinwhichWESwasperformed (upperplot),and6metastatic Figure S4. A C G Metastatic PBL Primary PBL ACA ACA ACC ACC ACG ACG ACT ACT CCA CCA T C>A CCC CCC C>A Primary PBL CCG CCG Mutationalsignaturesofprimaryandmetastatic PBL.A.Mutationalsignatures CCT CCT GCA GCA GCC GCC T C GCG GCG GCT GCT TCA TCA TCC TCC TCG TCG TCT TCT C G Strand directions ACA ACA ACC ACC ACG ACG ACT ACT A CCA CCA C>G C>G + CCC CCC G CCG CCG − CCT CCT GCA GCA GCC GCC GCG GCG GCT GCT TCA TCA TCC TCC TCG TCG TCT TCT ACA ACA C ACC ACC ACG ACG G ACT ACT T CCA CCA C>T CCC CCC C>T CCG CCG CCT CCT C GCA GCA GCC GCC GCG GCG Metastatic PBL T GCT GCT TCA TCA TCC TCC TCG TCG TCT TCT T C ATA ATA ATC ATC ATG ATG ATT ATT C CTA CTA T>A CTC CTC T>A Strand directions CTG CTG CTT CTT GTA GTA GTC GTC GTG GTG + GTT GTT G TTA TTA − TTC TTC T TTG TTG TTT TTT ATA ATA ATC ATC ATG ATG ATT ATT CTA CTA T>C CTC CTC T>C CTG CTG CTT CTT GTA GTA GTC GTC GTG GTG GTT GTT TTA TTA TTC TTC TTG TTG TTT TTT ATA ATA ATC ATC ATG ATG ATT ATT CTA CTA T>G CTC CTC T>G CTG CTG CTT CTT GTA GTA GTC GTC GTG GTG GTT GTT TTA TTA TTC TTC TTG TTG TTT TTT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 2122 PBL001_P PBL001_M1 PBL001_M2 PBL001_M3 PBL001_M4 PBL001_M5 PBL001_M6 PBL002_T PBL004_T PBL007_T PBL008_T PBL010_T CN Loss Gain Figure S5. SNP array-based copy number analysis of PBL. Copy number alterations in multiple samples from PBL001 and 5 primary tumor samples identified by SNP array analysis are shown. CN, copy number. A PBL001 PBL002 Chr11 Chr11 Hetero Hetero SNPs SNPs 1 1 0.5 0.5 ratio ratio Allelic Allelic 0 0 PBL003 PBL004 Chr11 Chr11 Hetero Hetero SNPs SNPs 1 1 ratio 0.5 0.5 ratio Allelic Allelic 0 0 PBL005 PBL006 Chr11 Chr11 Hetero Hetero SNPs SNPs 1 1 ratio 0.5 ratio 0.5 Allelic Allelic 0 0 PBL007 PBL008 Chr11 Chr11 Hetero Hetero SNPs SNPs 1 1 0.5 0.5 ratio ratio Allelic Allelic 0 0 B PBL010 PBL009 Chr11 Chr11 Hetero Hetero SNPs SNPs 1 1 0.5 0.5 ratio ratio Allelic Allelic 0 0 Figure S6. Targeted deep sequencing based allele-specific copy number analysis of chromosome 11. A. Sequencing-based copy number analysis identified CN-LOH with the minimal common region identical to chromosome 11p15.5 locus in 8 cases. Allelic ratios were calculated by allele frequencies and sequenced depths of SNPs; red/green dots represent major/minor allele frequencies at SNP sites. B. Allelic ratio of PBL009, the case with gain of whole chromosome 11, is also plotted. A PBL002 (tumor purity = 0.57) PBL004 (tumor purity = 0.86) TTCGTTCGTGGAAACGTTTC GGGTTATTTAA GTTACGCGTCGT TTC GTTC GTGGAAACGT TTCGGGTTAT TTAA GTTACGCGTCGT (T) (T) (T) (T) (T) (T) (T) PBL010 (tumor purity = 0.44) TTCGTTCGTGGAAACGTTTCGGGTTATTTAA GTTACGCGTCGT (T) (T) (T) (T) (T) (T) (T) B PBL009 (tumor purity = 0.69) TTCGTTCGTGGAAACGTTTCGGGTTAT TTAA GTTACGCGTCGT (T) (T) (T) (T) (T) (T) (T) Figure S7. Bisulfite Sanger sequencing of ICR1 on chromosome 11p15.5. A. Bisulfite Sanger sequencing of 3 CN-LOH (+) cases (CpG sites are indicated by arrows). From the estimated tumor purity (see Methods), all samples examined were considered homozygously methylated, indicating that their CN-LOH causes paternal uniparental disomy. B. Bisulfite sequencing of PBL009, the case with gain of whole chromosome 11. A B * M 3.0 IGF2 ACTB ** * * 2.5 IGF2 1000 bp 500 bp 2.0 100 bp 1.5 xpression of e e v 1.0 Relati PBL001 PBL005PBL009 PBL001 PBL005PBL009 0.5 Normal pancreas (Negative control) Normal pancreas (Negative control) 0 Normal PBL001 PBL005 PBL009 pancreas Sample Figure S8. Relative expression levels of IGF2 in CN-LOH-negative samples. A. Reverse transcription PCR was performed in normal pancreas (as a normal control), PBL001 (as a CN-LOH-positive control), PBL005, and PBL009. ACTB (encoding β-actin) was used as a reference gene. The lanes of negative control contain water control. M, DNA size markers. B. DNA bands were quantified using Image Lab software (Bio-Rad). Relative intensities of IGF2 compared to ACTB were computed, and mean intensities ± s.d. of 3 technical replicates are plotted with corresponding P-values (Student’s t-test). *P < 0.05, **P < 0.01. A 3 2 ratio 1 Log2 0 CN = 2 -1 -2 1 0.5 ratio Allelic 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19202122 Chromosome B C 3 2 1 ratio 0 Log2 -1 -2 ● 1 ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● 0.5 ● ●● ● ratio Allelic ● ● ● 0 Chr5 APC Figure S9. Aberrant activation of Wnt signaling pathway in PBL009. A. Sequencing-based copy number analysis identified CN-LOH within chromosome 5q involving APC gene locus. Upper panel shows the genome-wide copy number profile in PBL009. A log2 ratio of zero (horizontal red line) corresponds to a normal, diploid copy number.
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    (19) TZZ ¥¥_T (11) EP 2 285 833 B1 (12) EUROPEAN PATENT SPECIFICATION (45) Date of publication and mention (51) Int Cl.: of the grant of the patent: C07K 16/28 (2006.01) A61K 39/395 (2006.01) 17.12.2014 Bulletin 2014/51 A61P 31/18 (2006.01) A61P 35/00 (2006.01) (21) Application number: 09745851.7 (86) International application number: PCT/EP2009/056026 (22) Date of filing: 18.05.2009 (87) International publication number: WO 2009/138519 (19.11.2009 Gazette 2009/47) (54) AMINO ACID SEQUENCES DIRECTED AGAINST CXCR4 AND OTHER GPCRs AND COMPOUNDS COMPRISING THE SAME GEGEN CXCR4 UND ANDERE GPCR GERICHTETE AMINOSÄURESEQUENZEN SOWIE VERBINDUNGEN DAMIT SÉQUENCES D’ACIDES AMINÉS DIRIGÉES CONTRE CXCR4 ET AUTRES GPCR ET COMPOSÉS RENFERMANT CES DERNIÈRES (84) Designated Contracting States: (74) Representative: Hoffmann Eitle AT BE BG CH CY CZ DE DK EE ES FI FR GB GR Patent- und Rechtsanwälte PartmbB HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL Arabellastraße 30 PT RO SE SI SK TR 81925 München (DE) (30) Priority: 16.05.2008 US 53847 P (56) References cited: 02.10.2008 US 102142 P EP-A- 1 316 801 WO-A-99/50461 WO-A-03/050531 WO-A-03/066830 (43) Date of publication of application: WO-A-2006/089141 WO-A-2007/051063 23.02.2011 Bulletin 2011/08 • VADAY GAYLE G ET AL: "CXCR4 and CXCL12 (73) Proprietor: Ablynx N.V. (SDF-1) in prostate cancer: inhibitory effects of 9052 Ghent-Zwijnaarde (BE) human single chain Fv antibodies" CLINICAL CANCER RESEARCH, THE AMERICAN (72) Inventors: ASSOCIATION FOR CANCER RESEARCH, US, • BLANCHETOT, Christophe vol.10, no.
  • Supplementary Data

    Supplementary Data

    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.
  • Us 2018 / 0305689 A1

    Us 2018 / 0305689 A1

    US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e .
  • Comprehensive Genomic Analysis of Dietary Habits in UK Biobank Identifies Hundreds of Genetic Associations

    Comprehensive Genomic Analysis of Dietary Habits in UK Biobank Identifies Hundreds of Genetic Associations

    ARTICLE https://doi.org/10.1038/s41467-020-15193-0 OPEN Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations ✉ Joanne B. Cole 1,2,3, Jose C. Florez1,2,4 & Joel N. Hirschhorn1,3,5 Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as 1234567890():,; diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health. 1 Programs in Metabolism and Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA. 2 Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA. 3 Division of Endocrinology and Center for Basic and Translational Obesity Research, Boston Children’s Hospital, Boston, MA,

  • Two-Dimensional Enrichment Analysis for Mining High-Level Imaging Genetic Associations

    Brain Informatics (2017) 4:27–37 DOI 10.1007/s40708-016-0052-4 Two-dimensional enrichment analysis for mining high-level imaging genetic associations Xiaohui Yao . Jingwen Yan . Sungeun Kim . Kwangsik Nho . Shannon L. Risacher . Mark Inlow . Jason H. Moore . Andrew J. Saykin . Li Shen Received: 21 January 2016 / Accepted: 29 April 2016 / Published online: 13 May 2016 Ó The Author(s) 2016. This article is published with open access at Springerlink.com Abstract Enrichment analysis has been widely applied in expression data from Allen Human Brain Atlas and the genome-wide association studies, where gene sets imaging genetics data from Alzheimer’s Disease Neu- corresponding to biological pathways are examined for roimaging Initiative as test beds, we present an IGEA significant associations with a phenotype to help increase framework and conduct a proof-of-concept study. This statistical power and improve biological interpretation. In empirical study identifies 25 significant high-level two-di- this work, we expand the scope of enrichment analysis into mensional imaging genetics modules. Many of these brain imaging genetics, an emerging field that studies how modules are relevant to a variety of neurobiological path- genetic variation influences brain structure and function ways or neurodegenerative diseases, showing the promise measured by neuroimaging quantitative traits (QT). Given of the proposal framework for providing insight into the the high dimensionality of both imaging and genetic data, mechanism of complex diseases. we propose to study Imaging Genetic Enrichment Analysis (IGEA), a new enrichment analysis paradigm that jointly Keywords Imaging genetics Á Enrichment analysis Á considers meaningful gene sets (GS) and brain circuits Genome-wide association study Á Quantitative trait (BC) and examines whether any given GS–BC pair is enriched in a list of gene–QT findings.

  • Explorations in Olfactory Receptor Structure and Function by Jianghai

    Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 ABSTRACT Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 Copyright by Jianghai Ho 2014 Abstract Olfaction is one of the most primitive of our senses, and the olfactory receptors that mediate this very important chemical sense comprise the largest family of genes in the mammalian genome. It is therefore surprising that we understand so little of how olfactory receptors work. In particular we have a poor idea of what chemicals are detected by most of the olfactory receptors in the genome, and for those receptors which we have paired with ligands, we know relatively little about how the structure of these ligands can either activate or inhibit the activation of these receptors. Furthermore the large repertoire of olfactory receptors, which belong to the G protein coupled receptor (GPCR) superfamily, can serve as a model to contribute to our broader understanding of GPCR-ligand binding, especially since GPCRs are important pharmaceutical targets.